M MI

machine learning discrete math theory of computing puzzles

28-g DMC: Swan-song. Always PROVE your program works fully correctly all the time.

28-g DMC: Swan-song. Always PROVE your program works fully correctly all the time.
28-f DMC: Hardest problems in NP. NP-completeness.

28-f DMC: Hardest problems in NP. NP-completeness.
28-e DMC: Verifiable fast = NP (unbounded parallelism). Does fast=verifiable fast? P = NP question.

28-e DMC: Verifiable fast = NP (unbounded parallelism). Does fast=verifiable fast? P = NP question.
28-d DMC: The fuzzy region NP has problems whose solutions are

28-d DMC: The fuzzy region NP has problems whose solutions are "easy" to verify.
28-c DMC: Fast, slow and the fuzzy region in-between called NP (nondeterministic polynomial).

28-c DMC: Fast, slow and the fuzzy region in-between called NP (nondeterministic polynomial).
28-b DMC: Fast (polynomial) versus slow. Fast on a multi-tape TM is fast on a single-tape TM.

28-b DMC: Fast (polynomial) versus slow. Fast on a multi-tape TM is fast on a single-tape TM.
28-d LFD: Online decisions. Magdon's 1/e-theorem for dating.

28-d LFD: Online decisions. Magdon's 1/e-theorem for dating.
28-c LFD: Reinforcement learning and bandit problems: exploration versus exploitation.

28-c LFD: Reinforcement learning and bandit problems: exploration versus exploitation.
28-b LFD: The reinforcement learning setting.

28-b LFD: The reinforcement learning setting.
28-a DMC: What we did and the places you'll go.

28-a DMC: What we did and the places you'll go.
28-a LFD: Aggregation: boosting, bagging and blending. 1000 smurfs versus Einstein.

28-a LFD: Aggregation: boosting, bagging and blending. 1000 smurfs versus Einstein.
27-d DMC: Computability, a summary of the state of affairs.

27-d DMC: Computability, a summary of the state of affairs.
27-c DMC: Unsolvable problems: PCP (the domino puzzle), halting, program verification, ...

27-c DMC: Unsolvable problems: PCP (the domino puzzle), halting, program verification, ...
27-b DMC: Programmable Turing Machines: the computer.

27-b DMC: Programmable Turing Machines: the computer.
27-a DMC: Решатели, распознаватели и наброски машины Тьюринга.

27-a DMC: Решатели, распознаватели и наброски машины Тьюринга.
27-d LFD: A brief look at learning aides beyond PCA.

27-d LFD: A brief look at learning aides beyond PCA.
27-c LFD: Principal Components Analysis, PCA.

27-c LFD: Principal Components Analysis, PCA.
27-b LFD: Input preprocessing: centering, normalizing & whitening.

27-b LFD: Input preprocessing: centering, normalizing & whitening.
27-a LFD: Learning aides: feature scaling can affect learning outcome.

27-a LFD: Learning aides: feature scaling can affect learning outcome.
26-e LFD: The similarity for designing a kernel is task specific: strings, text, graphs, images.

26-e LFD: The similarity for designing a kernel is task specific: strings, text, graphs, images.
26-d LFD: What does a kernel compute? A scaled similarity!

26-d LFD: What does a kernel compute? A scaled similarity!
26-c LFD: SVM with tanh-kernel is a fully automatic 2-layer deep network.

26-c LFD: SVM with tanh-kernel is a fully automatic 2-layer deep network.
26-b LFD: SVM with RBF-kernel is a fully automatic RBF-network.

26-b LFD: SVM with RBF-kernel is a fully automatic RBF-network.
26-a LFD: SVM: Choosing your polynomial or Gaussian RBF kernel.

26-a LFD: SVM: Choosing your polynomial or Gaussian RBF kernel.
26-f DMC: Turing Machine encodings. Turing Machines can be listed. Turing Machines are countable.

26-f DMC: Turing Machine encodings. Turing Machines can be listed. Turing Machines are countable.
26-e DMC: Infinite loops. Deciders versus recognizers.

26-e DMC: Infinite loops. Deciders versus recognizers.
26-d DMC: A Turing Machine for multiplication. Deciders versus transducers.

26-d DMC: A Turing Machine for multiplication. Deciders versus transducers.
26-c DMC: Building our first Turing Machine.

26-c DMC: Building our first Turing Machine. "Machine-code" for solving repetition.
26-b DMC: High-level

26-b DMC: High-level "pseudocode" for a Turing Machine that solves repetition.
26-a DMC: Turing Machines. A mobile DFA with random access read-write memory (RAM).

26-a DMC: Turing Machines. A mobile DFA with random access read-write memory (RAM).